#' @title Classification Logistic Model Trees Learner
#' @author henrifnk
#' @name mlr_learners_classif.LMT
#'
#' @template class_learner
#' @templateVar id classif.LMT
#' @templateVar caller LMT
#'
#' @section Custom mlr3 defaults:
#' - `output_debug_info`:
#'   - original id: output-debug-info
#'
#' - `do_not_check_capabilities`:
#'   - original id: do-not-check-capabilities
#'
#' - `num_decimal_places`:
#'   - original id: num-decimal-places
#'
#' - `batch_size`:
#'   - original id: batch-size
#'
#' - Reason for change: This learner contains changed ids of the following control arguments
#' since their ids contain irregular pattern
#'
#' @references
#' Landwehr N, Hall M, Frank E (2005).
#' Logistic Model Trees
#' \url{https://link.springer.com/content/pdf/10.1007/s10994-005-0466-3.pdf}
#'
#' @export
LearnerClassifLMT = R6Class("LearnerClassifLMT",
  inherit = LearnerClassif,
  public = list(
    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function() {
      ps = ps(
          subset = p_uty(tags = c("train", "pars")),
          na.action = p_uty(tags = c("train", "pars")),
          B = p_lgl(default = FALSE, tags = c("train", "control")),
          R = p_lgl(default = FALSE, tags = c("train", "control")),
          C = p_lgl(default = FALSE, tags = c("train", "control")),
          P = p_lgl(default = FALSE, tags = c("train", "control")),
          I = p_int(lower = 1L, tags = c("train", "control")),
          M = p_int(default = 15L, lower = 1L, tags = c("train", "control")),
          W = p_dbl(default = 0, lower = 0, upper = 1, tags = c("train", "control")),
          A = p_lgl(default = FALSE, tags = c("train", "control")),
          doNotMakeSplitPointActualValue = p_lgl(default = FALSE,
            tags = c("train", "control")),
          output_debug_info = p_lgl(default = FALSE, tags = c("train", "control")),
          do_not_check_capabilities = p_lgl(default = FALSE,
            tags = c("train", "control")),
          num_decimal_places = p_int(default = 2L, lower = 1L,
            tags = c("train", "control")),
          batch_size = p_int(default = 100L, lower = 1L, tags = c("train", "control")),
          options = p_uty(default = NULL, tags = c("train", "pars"))
      )
      ps$add_dep("I", "C", CondEqual$new(FALSE))
      super$initialize(
        id = "classif.LMT",
        packages = "RWeka",
        feature_types = c("numeric", "factor", "ordered"),
        predict_types = c("response", "prob"),
        param_set = ps,
        properties = c("twoclass", "multiclass"),
        man = "mlr3extralearners::mlr_learners_classif.LMT"
      )
    }
  ),

  private = list(
    .train = function(task) {
      ctrl = self$param_set$get_values(tags = "control")
      if (length(ctrl) > 0L) {
        names(ctrl) = gsub("_", replacement = "-", x = names(ctrl))
        ctrl = mlr3misc::invoke(RWeka::Weka_control, ctrl)
      }

      pars = self$param_set$get_values(tags = "pars")
      f = task$formula()
      data = task$data()
      mlr3misc::invoke(RWeka::LMT, formula = f, data = data, control = ctrl, .args = pars)
    },

    .predict = function(task) {
      response = NULL
      prob = NULL
      newdata = task$data(cols = task$feature_names)

      if (self$predict_type == "response") {
        response = mlr3misc::invoke(predict, self$model, newdata = newdata, type = "class")
      } else {
        prob = mlr3misc::invoke(predict, self$model, newdata = newdata, type = "prob")
      }
      list(response = response, prob = prob)
    }
  )
)

.extralrns_dict$add("classif.LMT", LearnerClassifLMT)
